No Arabic abstract
In this work, we present the design of a superconducting, microwave quantum state router which can realize all-to-all couplings among four quantum modules. Each module consists of a single transmon, readout mode, and communication mode coupled to the router. The router design centers on a parametrically driven, Josephson-junction based three-wave mixing element which generates photon exchange among the modules communication modes. We first demonstrate SWAP operations among the four communication modes, with an average full-SWAP time of 760 ns and average inter-module gate fidelity of 0.97, limited by our modes coherences. We also demonstrate photon transfer and pairwise entanglement between the modules qubits, and parallel operation of simultaneous SWAP gates across the router. These results can readily be extended to faster and higher fidelity router operations, as well as scaled to support larger networks of quantum modules.
We propose an implementation of a quantum router for microwave photons in a superconducting qubit architecture consisting of a transmon qubit, SQUIDs and a nonlinear capacitor. We model and analyze the dynamics of operation of the quantum switch using quantum Langevin equations in a scattering approach and compute the photon reflection and transmission probabilities. For parameters corresponding to up-to-date experimental devices we predict successful operation of the router with probabilities above 94%.
Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing big data could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] was proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of 2-, 4-, and 8-dimensional vectors to different clusters using a small-scale photonic quantum computer, which is then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can in principle be scaled to a larger number of qubits, and may provide a new route to accelerate machine learning.
Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classical data into quantum states. Our implementation is based on a hybrid quantum computer, exploiting both discrete and continuous variables, for their capacity to encode novel features and efficiency of processing information. We propose encoding schemes that can realize well-known polynomial and Gaussian kernel ridge regressions, with exponentially speed-up regarding to the number of samples.
We present efficient quantum algorithms for simulating time-dependent Hamiltonian evolution of general input states using an oracular model of a quantum computer. Our algorithms use either constant or adaptively chosen time steps and are significant because they are the first to have time-complexities that are comparable to the best known methods for simulating time-independent Hamiltonian evolution, given appropriate smoothness criteria on the Hamiltonian are satisfied. We provide a thorough cost analysis of these algorithms that considers discretizion errors in both the time and the representation of the Hamiltonian. In addition, we provide the first upper bounds for the error in Lie-Trotter-Suzuki approximations to unitary evolution operators, that use adaptively chosen time steps.
Routing of photon play a key role in optical communication networks and quantum networks. Although the quantum routing of signals has been investigated in various systems both in theory and experiment, the general form of quantum routing with multi-output terminals still needs to be explored. Here, we propose an experimentally accessible tunable single-photon multi-channel routing scheme using a optomechanics cavity coulomb coupling to a nanomechanical resonator. The router can extract a single-photon from the coherent input signal directly modulate into three different output channels. More important, the two output signal frequencies can be selected by adjusting Coulomb coupling strength. We also demonstrate the vacuum and thermal noise will be insignificant for the optical performance of the single-photon router at temperature of the order of 20 mK. Our proposal may have paved a new avenue towards multi-channel router and quantum network.